Boosting the Immune System with Vitamin D: Special Focus on Prevention of COVID-19 and Complications

In addition to the classical functions of the musculoskeletal system and calcium homeostasis, the function of vitamin D as an immune modulator is well established. The vitamin D receptors and enzymes that metabolize vitamin D are ubiquitously expressed in most cells in the body, including T and B lymphocytes, antigen-presenting cells, monocytes, macrophages and natural killer cells that trigger immune and antimicrobial responses. Many in vitro and in vivo studies revealed that vitamin D promotes tolerogenic immunological action and immune modulation. Vitamin D adequacy positively influences the expression and release of antimicrobial peptides, such as cathelicidin, defensin, and anti-inflammatory cytokines, and reduces the expression of proinflammatory cytokines. Evidence suggestss that vitamin D's protective immunogenic actions reduce the risk, complications, and death from COVID-19. On the contrary, vitamin D deficiency worsened the clinical outcomes of viral respiratory diseases and the COVID-19-related cytokine storm, acute respiratory distress syndrome, and death. The study revealed the need for more preclinical studies and focused on well-designed clinical trials with adequate sizes to understand the role of vitamin D on the pathophysiology of immune disorders and mechanisms of subduing microbial infections, including COVID-19.Copyright © 2023 Bentham Science Publishers.

Measuring the Interference Effect of Bots in Disseminating Opposing Viewpoints Related to COVID-19 on Twitter Using Epidemiological Modeling

The activity of bots can influence the opinions and behavior of people, especially within the political landscape where hot-button issues are debated. To evaluate the bot presence among the propagation trends of opposing politically-charged viewpoints on Twitter, we collected a comprehensive set of hashtags related to COVID-19. We then applied both the SIR (Susceptible, Infected, Recovered) and the SEIZ (Susceptible, Exposed, Infected, Skeptics) epidemiological models to three different dataset states including, total tweets in a dataset, tweets by bots, and tweets by humans. Our results show the ability of both models to model the diffusion of opposing viewpoints on Twitter, with the SEIZ model outperforming the SIR. Additionally, although our results show that both models can model the diffusion of information spread by bots with some difficulty, the SEIZ model outperforms. Our analysis also reveals that the magnitude of the bot-induced diffusion of this type of information varies by subject. © 2023 IEEE Computer Society. All rights reserved.

Applying an Epidemiological Model to Evaluate the Propagation of Toxicity related to COVID-19 on Twitter

The prevalence of social media has increased the propagation of toxic behavior among users. Toxicity can have detrimental effects on users' emotion and insight and disrupt beneficial discourse. Evaluating the propagation of toxic content on social networks such as Twitter can provide the opportunity to understand the characteristics of this harmful phenomena. Identifying a mathematical model that can describe the propagation of toxic content on social networks is a valuable approach to this evaluation. In this paper, we utilized the SEIZ (Susceptible, Exposed, Infected, Skeptic) epidemiological model to find a mathematical model for the propagation of toxic content related to COVID-19 topics on Twitter. We collected Twitter data based on specific hashtags related to different COVID-19 topics such as covid, mask, vaccine, and lockdown. The findings demonstrate that the SEIZ model can properly model the propagation of toxicity on a social network with relatively low error. Determining an efficient mathematical model can increase the understanding of the dynamics of the propagation of toxicity on a social network such as Twitter. This understanding can help researchers and policymakers to develop methods to limit the propagation of toxic content on social networks. © 2022 IEEE Computer Society. All rights reserved.

Social Media Sentiment Analysis on Third Booster Dosage for COVID-19 Vaccination: A Holistic Machine Learning Approach

Over a period of more than two years the public health has been experiencing legitimate threat due to COVID-19 virus infection. This article represents a holistic machine learning approach to get an insight of social media sentiment analysis on third booster dosage for COVID-19 vaccination across the globe. Here in this work, researchers have considered Twitter responses of people to perform the sentiment analysis. Large number of tweets on social media require multiple terabyte sized database. The machine learned algorithm-based sentiment analysis can actually be performed by retrieving millions of twitter responses from users on daily basis. Comments regarding any news or any trending product launch may be ascertained well in twitter information. Our aim is to analyze the user tweet responses on third booster dosage for COVID-19 vaccination. In this sentiment analysis, the user sentiment responses are firstly categorized into positive sentiment, negative sentiment, and neutral sentiment. A performance study is performed to quickly locate the application and based on their sentiment score the application can distinguish the positive sentiment, negative sentiment and neutral sentiment-based tweet responses once clustered with various dictionaries and establish a powerful support on the prediction. This paper surveys the polarity activity exploitation using various machine learning algorithms viz. Naïve Bayes (NB), K- Nearest Neighbors (KNN), Recurrent Neural Networks (RNN), and Valence Aware wordbook and sEntiment thinker (VADER) on the third booster dosage for COVID-19 vaccination. The VADER sentiment analysis predicts 97% accuracy, 92% precision, and 95% recall compared to other existing machine learning models. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Detection of Covid-19 using Cough Sounds

Coronavirus, the pandemic due to which about 4 million have lost their lives and counting, is still on. Many scientists and researchers are trying to find ways to detect coronavirus as soon as possible in the human body so that they can start their medication and precaution as soon as possible. Still, due to lack of lab facilities, the RT-PCR is taking more than three days to give the report, and in the meanwhile, patients get serious and life in danger. So in this paper, we proposed an audio-based coronavirus detection technique in which we can get results in minutes. Coronavirus is a respiratory disease, and the sound produced while breathing can tell us about the presence of coronavirus. Audio-based detection was already used for the detection of asthma, pneumonia. So, in this paper, we implemented a combination of machine learning and deep learning techniques to find the presence of Covid-19, and the model has an accuracy of 78% and an f1 score of 74%. This technique can be used as a starting point for just audio data to diagnose diseases and save lives. © 2022, American Scientific Publishing Group (ASPG). All rights reserved.

Photocatalytic mineralization of pharmaceutical contaminants in water by heterogeneous photocatalysis in the presence of LED-irradiation: A green and sustainable approach

There is an urgent need to mineralize organic pollutants such as antibiotics and other toxic organic materials discharged by various pharmaceutical and chemical industries into freshwater. These organic pollutants affect the aquatic ecosystem and human health due to bioaccumulation in the food chains and food webs. Heterogeneous photocatalysis is an advanced oxidation-based green technique by which these pharmaceutics can be degraded in the wastewater by using nontoxic and eco-friendly catalysts in the presence of light-emitting diodes (LEDs) as a source of irradiation. The main aim of the chapter is to disseminate information regarding the degradation of antibiotics and other pharmaceutics by green, non-toxic, and effective catalysts via economically viable techniques. Antibiotics are oxidized by hydroxyl radicals and superoxides generated during the irradiation of light on the surface of the catalyst. In this chapter, the authors discuss the most commonly prescribed antibiotics in the COVID-19 pandemic and how these antibiotics become environmental contaminants. They have also proposed the mechanism of degradation of these antibiotics in the presence of LED irradiation to attain a green and sustainable environment. © 2023 Nova Science Publishers, Inc. All rights reserved.

Time Series based Models for Corona Data Analytics

There is a great challenge to deal with prediction of an epidemic or pandemic in the future through artificial intelligence or state-of-art technology. This is evident in the case of pandemic happened from January 2020 which is a result of corona virus. In early stages of covid-19 caused by corona virus, the symptoms are not severe and mostly cured through self-medication. In this situation, estimating the real spread based on the reports from various hospitals might be misleading. There might be lot of variation in the reports based on different types of measurements performed, and the tests conducted on only the symptomatic patients. In spite of all these constraints, a huge amount of covid-19 related data is published since 3 years and also updated on a daily basis. This serves as a motivation to consider various mathematical models to predict the course of change in an epidemic and result in effective control strategies. The challenge is to predict the peak and end of the epidemic together with its evolution through available incomplete data and intrinsic complexity. In this paper, time series models are proposed to analyze corona spread data and analyzing its impact based on gender, age and geographical location. The proposed algorithm leverages machine learning models to predict number of corona cases in the future. An early detection of spread of corona would help in stopping community transmission and this serves a major motivation for this research. ARIMA model and Recurrent Neural Networks (RNN) based LSTM model perform way better than the machine learning models based on regression and decision trees. © 2022 IEEE.

Classification of Chest Radiography Scans for COVID-19

Humanity has suffered as a result of the COVID-19 pandemic for more than two years. Testing kits were not widely accessible during the pandemic, which caused alarm. Any technical development that enables a quicker and more accurate identification of COVID-19 infection can be very beneficial for the medical field. X-rays can be used to examine a patient's lungs since COVID-19 targets the epithelial cells that line the respiratory system. It is challenging to determine COVID-19 from other Viral Pneumonia cases, though. The purpose of this paper is to examine the effectiveness of deep learning models in the quick and precise detection of COVID-19 in chest X-ray scans. © 2022 IEEE.

Maternal and perinatal outcome in Covid-19 complicated pregnancies in a Level-3 Covid facility of North India

Background: Due to its physiologic immune suppression, pregnancy is a vulnerable time for severe respiratory infections including COVID-19. However, information regarding the effect of COVID-19 during pregnancy is limited. Objectives: To study the clinical profile of patients suffering from coronavirus disease-2019 (COVID-19) during pregnancy and to evaluate the effect of COVID-19 on maternal, perinatal, and neonatal outcomes. Methodology: This is a cross-sectional observational study over a period of one year from June 2020 to May 2021, in Level-3 Covid facility in Ghaziabad. All pregnant females with confirmed positive for Corona virus infection admitted to the covid ward under the department of Obstetrics & Gynecology were included in the study. Results: A total of 233 pregnant women were included in the study. Maximum patients were from age group 21-30 years (53.2), multigravida (62.7%), and presented in the third trimester (80.7%). On admission, 198 patients (85%) had no covid related symptoms and only three patients had severe symptoms requiring ICU care. Total 102 patients delivered (43.77%), out of whom 40 had a normal vaginal delivery and 62 had a cesarean section. The incidence of preterm birth was 22.5% and maternal death was in three patients (1.3%). Conclusion: The common presentation of COVID-19 during pregnancy is either a mild disease or even asymptomatic. The maternal outcomes observed in late pregnancy and fetal and neonatal outcomes appear good in most cases. Further studies are required to know long-term outcomes and potential intrauterine vertical transmission. © 2022 Medical Journal of Dr. D.Y. Patil Vidyapeeth ;Published by Wolters Kluwer - Medknow.

Contract Cheat Detection using Biometric Keystroke Dynamics

Contract cheating has become a profound issue in academics with the onset of the COVID-19 pandemic as digitised evaluation has become common practice. This evaluation method opens up for examining students remotely, either by online home exams or longer written assessments done away from the classroom. Contract cheating refers to a problem where the students hire a third party to complete their assignment and submit it for grading as their own. Manually dealing with contract cheating is a cumbersome task and tools for plagiarism detection are not able to detect contract cheaters as students do not use the work of other authors without consent. In this paper, a machine learning based system is designed to specifically detect the cases of contract cheating in academics. The system uses keystroke biometric behaviour where typing style is analysed to discriminate cheaters from genuine students. The experiments are conducted on two datasets where one is existing and another is designed by performing data collection in a university for recording the keystroke features. Two categories of keystroke dynamics, namely duration and latency-based features are studied for designing the various machine learning-based systems for investigating the efficient one. Furthermore, the performance of the systems are evaluated under the setting of zero false accusations in order to avoid genuine students being charged as imposters. © 2022 IEEE.

Mental health care using video during COVID-19: service user and clinician experiences, including future preferences

Purpose: This paper aims to evaluate service user (SU) and clinician acceptability of video care, including future preferences to inform mental health practice during COVID-19, and beyond. Design/methodology/approach: Structured questionnaires were co-developed with SUs and clinicians. The SU online experience questionnaire was built into video consultations (VCs) via the Attend Anywhere platform, completed between July 2020 and March 2021. A Trust-wide clinician experience survey was conducted between July and October 2020. Chi-squared test was performed for any differences in clinician VC rating by mental health difficulties, with the content analysis used for free-text data. Findings: Of 1,275 SUs completing the questionnaire following VC, most felt supported (93.4%), and their needs were met (90%). For future appointments, 51.8% of SUs preferred video, followed by face-to-face (33%), with COVID-related and practical reasons given. Of 249 clinicians, 161 (64.7%) had used VCs. Most felt the therapeutic relationship (76.4%) and privacy (78.7%) were maintained. Clinicians felt confident about clinical assessment and management using video. However, they were less confident in assessing psychotic symptoms and initiating psychotropic medications. There were no significant differences in clinician VC rating by mental health difficulties. For future, more SUs preferred using video, with a quarter providing practical reasons. Originality/value: The study provides a real-world example of video care implementation. In addition to highlighting clinician needs, support at the wider system/policy level, with a focus on addressing inequalities, can inform mental health care beyond COVID-19. © 2022, Emerald Publishing Limited.

Psychological stress and worsening diabetes: An endemic within the pandemic!

Introduction: Strict isolation measures and interrupted healthcare services during the COVID-19 pandemic are contemplated to instigate stress universally, particularly in those with chronic illnesses such as Type 1 Diabetes (T1D). Objective(s): To evaluate the determinants of stress and its impact on glycemic control among Indian adolescents and young adults (aged 10-25 years), living with T1D. Method(s): A cross-sectional observational study using online, semistructured survey, including Perceived Stress Scale (PSS-10). Result(s): A total of 97 patients (49 males;mean age 18.8 +/- 4.5 years, mean diabetes duration 8.0 +/- 5.0 years;mean HbA1c 8.1 +/- 1.5%) were analyzed. Age (y) (r = 0.325, p = 0.005) and HbA1c (%) within the preceding 3 months (r = 0.274, p = 0.036) correlated positively with PSS-10 score, Figure 1. There was a statistically significant difference in PSS-10 score based on gender (t [70] = -2.147;p = 0.035), education (F [4,67] = 4.34, p = 0.003) and occupation (F [3,68] = 4.50, p = 0.006). On multiple linear regression, gender, occupation and HbA1c were the significant determinants of PSS-10 (F [3,55] =12.01, p < 0.001, R2 = 0.363). One-way ANOVA showed a significant impact of mean PSS-10 score on the glycemic control (F [2,69] = 3.813, p = 0.027). Conclusion(s): Female gender, salaried individuals, and pre-existing poorly controlled diabetes contributed to an increased risk of stress. Increased stress resulted in worsened glycemic control.

A Systematic Approach for Contextualizing Focal Structure Analysis in Social Networks

Focal Structures are key sets of individuals who may be responsible for coordinating events, protests, or leading citizen engagement efforts on social media networks. Discovering focal structures that can promote online social campaigns is important but complex. Unlike influential individuals, focal structures can effect large-scale complex social processes. In our prior work, we applied a greedy algorithm and bi-level decomposition optimization solution to identify focal structures in social media networks. However, the outcomes lacked a contextual representation of the focal structures that affected interpretability. In this research, we present a novel Contextual Focal Structure Analysis (CFSA) model to enhance the discovery and the interpretability of the focal structures to provide the context in terms of the content shared by individuals in the focal structures through their communication network. The CFSA model utilizes multiplex networks, where the first layer is the users-users network based on mentions, replies, friends, and followers, and the second layer is the hashtag co-occurrence network. The two layers have interconnections based on the user hashtag relations. The model's performance was evaluated on real-world datasets from Twitter related to domestic extremist groups spreading information about COVID-19 and the Black Lives Matter (BLM) social movement during the 2020–2021 time. The model identified Contextual Focal Structure (CFS) sets revealing the context regarding individuals’ interests. We then evaluated the model's efficacy by measuring the influence of the CFS sets in the network using various network structural measures such as the modularity method, network stability, and average clustering coefficient values. The ranking Correlation Coefficient (RCC) was used to conduct a comparative evaluation with real-world scenarios. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Human Emotion Prediction Analysis on Post-COVID-19 Crisis in Digital Media Using Deep Learning

The COVID-19 pandemic has produced a significant impact on society. Apart from its deadliest attack on human health and economy, it has also been affecting the mental stability of human being at a larger scale. Though vaccination has been partially successful to prevent further virus outreach, it is leaving behind typical health-related complications even after surviving from the disease. This research work mainly focuses on human emotion prediction analysis in post-COVID-19 period. In this work, a considerable amount of data collection has been performed from various digital sources, viz. Facebook, e-newspapers, and digital news houses. Three distinct classes of emotion, i.e., analytical, depressed, and angry, have been considered. Finally, the predictive analysis is performed using four deep learning models, viz. CNN, RNN, LSTM, and Bi-LSTM, based on digital media responses. Maximum accuracy of 97% is obtained from LSTM model. It has been observed that the post-COVID-19 crisis has mostly depressed the human being. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

A Novel Deep Learning Algorithm for Covid Detection and Classification

The prediction of future development of a natural phenomenon is one of the main objectives of recent technology, but this is a great challenge when dealing with an epidemic or pandemic. This proved to be particularly true in the case of Covid-19 global pandemic that the world is suffering and facing since January 2020. The response to the virus infection are partially known, however the immune system is mostly affected especially in patients with pre-existing respiratory or systemic diseases. Most infections by coronavirus are mild and self-treated. Therefore, in early stages of the disease, it will be misleading to estimate the real spread of the virus based on the reports of hospital. Moreover, such reports vary according to how measurements are performed, and the number of tests related only to the number of symptomatic patients. Despite all this, the large amount of official data published in last months, and updated daily has motivated various mathematical models, which are required to predict the evolution of an epidemic and plan effective control strategies. Due to the incompleteness of the data and intrinsic complexity, predicting the evolution, the peak or the end of the pandemic is a challenge. In this paper, a deep learning based approach is proposed aiming to evaluate a-priori risk of an epidemic caused by Covid-19. The proposed algorithm leverages image processing and deep learning algorithms to detect Covid and differentiate between normal, Covid affected, lung opacity and viral pneumonia affected chest x-rays. This results in setting strategies to prevent or decrease the impact of future epidemic waves. The accuracy for the proposed algorithm is 95.01% and Recall is 98.5% on validation data. The inference is that combining image processing with deep learning can improve performance of Covid detection. © 2022 IEEE.

Pulmonary Cystic Lesions in Patients with COVID-19 Infection: A Case Series

Computed Tomography has played a vital role in Coronavirus Disease 2019 (COVID-19) infection, caused by Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) over the last two years. The typical features of COVID-19 on High Resolution Computed Tomography (HRCT) of chest including ground glass opacities and consolidation with a peripheral and lower lobar predilection have been very well documented in literature worldwide. However, thin-walled lucencies in the lung parenchyma called cysts is not very well documented. Authors thus present a case series comprising six SARS-CoV-2 Reverse Transcription-Polymerase Chain Reaction (RT-PCR) positive patients admitted to the hospital during the period 1stApril 2021 to 31stMay 2021 with lung cysts on HRCT. It was a retrospective study wherein details of the patients were drawn from the case record sheets and the clinical parameters along with HRCT chest findings were analysed, and correlations were drawn to study the cause, timing and significance of these cysts. In this study, the cysts were found to be thin-walled, varying in size from 5-20 mm in diameter and subpleural in distribution with no obvious lobar predilection.The immediately surrounding lung parenchyma showed features of maximal involvement by the atypical pneumonitis. All six cases had moderate to severe lung involvement entailing oxygen therapy. The high flow oxygen therapy and its duration along with degree of lung involvement, are important determinants of cystic degeneration. In the present case series, cystic changes were observed somewhere between day 15 to day 40 of the disease and thus a part of postacute fibrosis in COVID-19 infection.

Covid-19 and Awareness of the Society: A Collection of the Important Facts and Figures Related to the Global Pandemic

Our article COVID-19 AWARENESS is created to provide latest and correct information regarding the current pandemic situation. It provides the public with statistics of active, recovered, and death cases country-wise, all around the world. It also provides them with latest news regarding the pandemic every 24 h. It also provides helpline numbers with search functions for the people to call for help. Our site also provides guidelines on preventive measures along with the steps that a person should follow if they are infected with the coronavirus. The other guidelines are displayed on the Web site in an attractive and responsive way. We also provide a search function for helpline numbers which searches on the basis on the name of a states or a part of it. Our article provides all the necessary information to the public that are required to be healthy and safe in these difficult times for the humanity because of the pandemic. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Chest Radiography in COVID-19 Pregnancy and Its Clinico-biochemical Correlation: A Retrospective Single-center Study

Aim: We have witnessed diverse presentations of coronavirus disease-2019 (COVID-19) in pregnant females during first and second waves. The aim of this study was to evaluate the usefulness of chest X-ray and its correlation of severity scoring with clinical, laboratory parameters and maternal-fetal outcome during management of COVID-19 pregnant women in low resource settings. Methodology: This was a retrospective observational study conducted at the Government Institute of Medical Sciences, Greater Noida, from May 2020 to May 2021. The study included 185 pregnant women in second and third trimesters with reverse transcription-polymerase chain reaction (RT-PCR)-confirmed COVID-19 disease. The chest radiographs of all patients were analyzed and severity scoring was done using modified radiographic assessment of lung edema (RALE) criteria. The correlation of severity index with clinical and biochemical profile of patients with normal and abnormal X-ray findings was compared. Two-tailed p-value of <0.05 was considered significant in our study. Results: Out of 185 patients, 38 had abnormal X-ray findings, whereas 147 had normal X-ray. A significant difference was observed in mean values of lactate dehydrogenase (LDH), ferritin, C-reactive protein (CRP), D-dimer, total leukocyte count (TLC), and interleukin 6 (IL-6) levels across both X-ray groups. The proportion of pregnant mothers with live birth, high-risk pregnancy, steroid treatment, oxygen supplementation, invasive ventilation, and number of presenting symptoms varied statistically across both the X-ray groups (p-value <0.05). Receiver-operating characteristic (ROC) analysis revealed that an X-ray score of “5.5” has the best prognostic significance of maternal death with sensitivity of 87.5 and 96.6% specificity. Conclusion: Chest radiography for the assessment of disease status in COVID-19 pregnancies is an effective and affordable alternative to CT scan in low resource settings.

Developing Approaches to Detect and Mitigate COVID-19 Misinfodemic in Social Networks for Proactive Policymaking

Throughout the COVID-19 pandemic, people have grown more reliant on social media for obtaining news, information, and entertainment. However, the information environment has become a breeding ground for disinformation tactics. Formal recommendations from medical experts are becoming muffled by the avalanche of toxic content and social media echo chambers are being created in hopes that users only consume stories that support certain beliefs. Despite the advantages of utilizing online social networks (OSNs), a consensus is emerging suggesting the presence of an ever-growing population of malicious actors who utilize these networks to spread misinformation and harm others. These actors are using advanced techniques and are engaging on multiple platforms to propagate their disinformation campaigns. As such, researchers have had to evolve their methods to detect disinformation. In this chapter, we present novel multimethod socio-computational approaches to analyze disinformation content and actors on OSNs during the initial months after COVID-19 was made public. These techniques are presented as case studies in narrative analysis of COVID-19 misinformation themes on blogs, identifying anti-lockdown protestor coordination through connective action on Twitter, analysis of hate speech and divisive discourse on YouTube through toxicity analysis, and modeling of misinformation contagion using an epidemiological approach. We end the chapter by presenting a COVID-19 misinformation tracker tool developed in collaboration with the Arkansas Office of the Attorney General. Our results offer policymakers valuable data to make informed decisions about the information environment and derive appropriate and timely countermeasures to combat insidious forms of cyber threats. Our efforts demonstrate that when researchers coordinate with policymakers it can make a difference, especially when that coordination remains an ongoing process. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

Comparative Predictive Analysis of Mortality Rate after Covid-19 Vaccination Using Various Machine Learning Approaches

This work has mainly targeted in performing comparative real time predictive analysis of mortality rate after having COVID-19 vaccination using different machine learning approaches. In this paper various deep learning models viz. RNN, LSTM and CNN have been utilized to make future prediction on mortality rate on the basis of administered vaccine doses. Firstly, the dataset of confirmed active cases, death cases and administered vaccine doses have been converted from time-series format to supervised learning format, and secondly different deep learning models have been trained and compared based on the transformed dataset. The prediction analysis is performed strictly based on the newest COVID-19 Delta Variant infected cases. The predictive analysis has resulted 15.53% of reduction in mortality rate and 24.67% of reduction in confirmed active cases with increase in vaccination rate. © 2022 IEEE.